a473c777983b7f2e123f996c9b15607cb1979701,onmt/ModelConstructor.py,,load_test_model,#Any#Any#,117

Before Change

def load_test_model(opt, dummy_opt):
    checkpoint = torch.load(opt.model,
                            map_location=lambda storage, loc: storage)
    fields = onmt.io.load_fields_from_vocab(
        checkpoint["vocab"], data_type=opt.data_type)

    model_opt = checkpoint["opt"]
    for arg in dummy_opt:
        if arg not in model_opt:
            model_opt.__dict__[arg] = dummy_opt[arg]

    model = make_base_model(model_opt, fields,
                            use_gpu(opt), checkpoint)
    model.eval()
    model.generator.eval()
    return fields, model, model_opt


def make_base_model(model_opt, fields, gpu, checkpoint=None):
    

After Change

    if model_path is None:
        model_path = opt.model
    checkpoint = torch.load(model_path,
                            map_location=lambda storage, loc: storage)
    fields = onmt.io.load_fields_from_vocab(
        checkpoint["vocab"], data_type=opt.data_type)

    model_opt = checkpoint["opt"]
    for arg in dummy_opt:
        if arg not in model_opt:
            model_opt.__dict__[arg] = dummy_opt[arg]

    model = make_base_model(model_opt, fields,
                            use_gpu(opt), checkpoint)
    model.eval()
    model.generator.eval()
    return fields, model, model_opt


def make_base_model(model_opt, fields, gpu, checkpoint=None):
    
    Args:
        model_opt: the option loaded from checkpoint.
        fields: `Field` objects for the model.
        gpu(bool): whether to use gpu.
        checkpoint: the model gnerated by train phase, or a resumed snapshot
                    model from a stopped training.
    Returns:
        the NMTModel.
    
    assert model_opt.model_type in ["text", "img", "audio"], \
        ("Unsupported model type %s" % (model_opt.model_type))

    // Make encoder.

Instances